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1. Field of the Invention
This invention relates to the field of combinatorial chemistry screening libraries and more specifically to: 1) a method of validating the molecular structural descriptors necessary for designing an optimal combinatorial screening library; 2) a method of designing an optimal combinatorial screening library; 3) a method of merging libraries derived from different combinatorial chemistries; and 4) methods of following up and optimizing identified leads. The libraries designed by the method are constructed to ensure that an optimal structural diversity of compounds is represented. In particular, the invention describes the design of libraries of small molecules to be used for pharmacological testing.
2. Description of Related Art
Statement of the Problem
While the present invention is discussed with detailed reference to the search for and identification of pharmacologically useful chemical compounds, the invention is applicable to any attempt to search for and identify chemical compounds which have some desired physical or chemical characteristic(s). The broader teachings of this invention are easily recognized if a different functional utility or useful property describing other chemical systems is substituted below for the term “biological activity”.
Starting with the serendipitous discovery of penicillin by Fleming and the subsequent directed searches for additional antibiotics by Waksman and Dubos, the field of drug discovery during the post World War II era has been driven by the belief that nature would provide many needed drugs if only a careful and diligent search for them was conducted. Consequently, pharmaceutical companies undertook massive screening programs which tested samples of natural products (typically isolated from soil or plants) for their biological properties. In a parallel effort to increase the effectiveness of the discovered “lead” compounds, medicinal chemists learned to synthesize derivatives and analogs of the compounds. Over the years, as biochemists identified new enzymes and biological reactions, large scale screening continued as compounds were tested for biological activity in an ever rapidly expanding number of biochemical pathways. However, proportionately fewer and fewer lead compounds possessing a desired therapeutic activity have been discovered. In an attempt to extend the range of compounds available for testing, during the last few years the search for unique biological materials has been extended to all corners of the earth including sources from both the tropical rain forests and the ocean. Despite these and other efforts, it is estimated that discovery and development of each new drug still takes about 12 years and costs on the order of 350 million dollars.
Beginning approximately twenty-five years ago, as bioscientists learned more about the chemical and stereochemical requirements for biological interactions, a variety of semi-empirical, theoretical, and quantitative approaches to drug design were developed. These approaches were accelerated by the availability of powerful computers to perform computational chemistry. It was hoped that the era of “rational drug design” would shorten the time between significant discoveries and also provide an approach to discovering compounds active in biological pathways for which no drugs had yet been discovered. In large part, this work was based on the accumulated observation of medicinal chemists that compounds which were structurally similar also possessed similar biological activities. While significant strides were made using this approach, it too, like the mass screening programs, failed to provide a solution to the problem of rapidly discovering new compounds with activities in the ever increasing number of biological pathways being elucidated by modern biotechnology.
During the past four or five years, a revised screening approach has been under development which, it was hoped, would accelerate the pace of drug discovery. In fact, the approach has been remarkably successful and represents one of the most active areas in biotechnology today. This new approach utilizes combinatorial libraries against which biological assays are screened. Combinatorial libraries are collections of molecules generated by synthetic pathways in which either: 1) two groups of reactants are combined to form products; or 2) one or more positions on core molecules are substituted by a different chemical constituent/moiety selected from a large number of possible constituents.
Two fundamental ideas underlie combinatorial screening libraries. The first idea, common to all drug research, is that somewhere amongst the diversity of all possible chemical structures there exist molecules which have the appropriate shape and binding properties to interact with any biological system. The second idea is the belief that synthesizing and testing many molecules in parallel is a more efficient way (in terms of time and cost) to find a molecule possessing a desired activity than the random testing of compounds, no matter what their source. In the broadest context, these ideas require that, since the binding requirements of a ligand to the biological systems under study (enzymes, membranes, receptors, antibodies, whole cell preparations, genetic materials, etc.) are not known, the screened compounds should possess as broad a range of characteristics (chemical and physical) as possible in order to increase the likelihood of finding one that is appropriate for any given biological target. This requirement for a screening library is reflected in the term “diversity”—essentially a way of suggesting that the library should contain as great a dissimilarity of compounds as possible.
However, as is immediately apparent, a combinatorial approach to synthesizing molecules generates an immense number of compounds many with a high degree of structural similarity. In fact, the number of compounds synthetically accessible with known organic reactions exceeds by many orders of magnitude the numbers which can actually be made and tested. One area where these ideas were first explored is in the design of peptide libraries. For a library of five member peptides synthesized using the 20 naturally occurring amino acids, 3,200,000, (205) different peptides may be constructed. The number of combinatorial possibilities increases even more dramatically when non-peptide combinatorial libraries are considered. With non-peptide libraries, the whole synthetic chemical universe of combinatorial possibilities is available. Library sizes ranging from 5×107 to 4×1012 molecules are now being discussed. The enormous universe of chemical compounds is both a blessing and a curse to medicinal chemists seeking new drugs. On the one hand, if a molecule exists with the desired biological activity, it should be included in the chemical universe. On the other hand, it may be impossible to find. Thus, the principal focus of recent efforts has been to define smaller screening subsets of molecules derivable from accessible combinatorial syntheses without losing the inherent diversity of an accessible universe.
To date, in order to narrow the focus of the search and reduce the number of compounds to be screened, attention has been directed to designing biologically specific libraries. Thus, many combinatorial screening libraries existing in the prior art have been designed based on prior knowledge about a particular biological system such as a known pharmacophore (a geometric arrangement of structural fragments abstracted from molecular structures known to have activity). Even with this knowledge, molecules are included in these prior art libraries based on intuition—“seat of the pants” estimations of likely similarity based on an intuitive “feel” for the systems under study. This procedure is essentially pseudo-random screening, not rational library design. Several biotechnology startup companies have developed just such proprietary libraries, and success using combinatorial libraries has been achieved by sheer effort. In one example 18 libraries containing 43 million compounds were screened to identify 27 active compounds1. With library searches of this magnitude, it is most likely that the enormous number of inactive molecules [(43×106)−27] must have included staggering numbers of redundantly inactive molecules—molecules not significantly distinguishable from one another—even in libraries designed with a particular biological target in mind. Clearly, when searching for a lead molecule which interacts with an uncharacterized biological target, approaches requiring knowledge of the biological targets will not work. But finding such a lead is exactly the case for which it is hoped general purpose screening libraries can be designed. If the promise of combinatorial chemistry is ever to be fully realized, some rational and quantitative method of reducing the astronomical number of compounds accessible in the combinatorial chemistry universe to a number which can be usefully tested is required. In other words, the efficiency of the search process must be increased. For this purpose, a smaller rationally designed screening library, which still retains the diversity of the combinatorially accessible compounds, is absolutely necessary.
Thus, there are two criteria which must be met by any screening library subset of some universe of combinatorially accessible compounds. First, the diversity, the dissimilarity of the universe of compounds accessible by some combinatorial reaction, must be retained in the screening subset. A subset which does not contain examples of the total range of diversity in such a universe would potentially miss critical molecules, thereby frustrating the very reason for the creation of the subset. Second, for efficient screening, the ideal subset should not contain more than one compound representative of each aspect of the diversity of the larger group. If more than one example were included, the same diversity would be tested more than once. Such redundant screening would yield no new information while simultaneously increasing the number of compounds which must be synthesized and screened. Therefore, the fundamental problem is how to reduce to a manageable number the number of compounds that need to be synthesized and tested while at the same time providing a reasonably high probability that no possible molecule of biological importance is overlooked. (In this regard, it should be recognized that the only way of absolutely insuring that all diversity is represented in a library is to include and test all compounds.) A conceptual analogy to the problem might be: what kind of filter can be constructed to sort out from the middle of a blinding snowstorm individual snowflakes which represent all the classes of crystal structures which snowflakes can form?
The fundamental question plaguing progress in this area has been whether the concept of the diversity of molecular structure can be usefully described and quantified; that is, how is it possible to compare/distinguish the physical and chemical properties determinative of biological activity of one molecule with that of another molecule? Without some way to quantitatively describe diversity, no meaningful filter can be constructed. Fortunately, for biological systems, the accumulated wisdom of bioscientists has recognized a general principle alluded to earlier which provides a handle on this problem. As framed by Johnson and Maggiora2, the principle is simply stated as: “structurally similar molecules are expected to exhibit similar (biological) properties.” Based on this principle, quantifying diversity becomes a matter of quantifying the notion of structural similarity. Thus, for design of a screening subset of a combinatorial library (hereafter referred to as a “combinatorial screening library”), it should only be necessary to identify which molecules are structurally similar and which structurally dissimilar. According to the selection criteria outlined above, one molecule of each structurally similar group in the combinatorially accessible chemical universe would be included in the library subset. Such a library would be an optimally diverse combinatorial screening library. The problem for medicinal chemists is to determine how the intuitively perceived notions of structural similarity of chemical compounds can be validly quantified. Once this question is satisfactorily answered, it should be possible to rationally design combinatorial screening libraries.
Prior Art Approaches
Many descriptors of molecular structure have been created in the prior art in an attempt to quantify structural similarity and/or dissimilarity. As the art has recognized, however, no method currently exists to distinguish those descriptors that quantify useful aspects of similarity from those which do not. The importance of being able to validate molecular descriptors has been a vexing problem restricting advances in the art, and, before this invention, no generally applicable and satisfactory answer had been found. The problem may be conceptualized in terms of a multidimensional space of structurally derivable properties which is populated by all possible combinatorially accessible chemical compounds. Compounds lying “near” one another in any one dimension may lie “far apart” from one another in another dimension. The difficulty is to find a useful design space—a quantifiable dimensional space (metric space) in which compounds with similar biological properties cluster; ie., are found measurably near to each other. What is desired is a molecular structural descriptor which, when applied to the molecules of the chemical universe, defines a dimensional space in which the “nearness” of the molecules with respect to a specified characteristic (ie.; biological activity) in the chemical universe is preserved in the dimensional space. A molecular structural descriptor (metric) which does not have this property is useless as a descriptor of molecular diversity. A valid descriptor is defined as one which has this property.
In light of the above, it should be noted that there is a difference between a descriptor being valid and being perfect. There may or may not be a “perfect” metric which precisely and quantitatively maps the diversity of compounds (much less those of biological interest). However, a good approximation is sufficient for purposes of designing a combinatorial screening library and is considered valid/useful. Acceptance of this validation/usefulness criteria is essentially equivalent to saying that, if there is a high probability that if one molecule is active (or inactive), a second molecule is also active (or inactive), then most of the time sampling one of the pair will be sufficient. Restating this same principle with a slightly different emphasis highlights another feature, namely: the design criteria for combinatorial screening libraries should yield a high probability that, for any given inactive molecule, it is more probable to find an active molecule somewhere else rather than as a near neighbor of that inactive molecule. While this is a probabilistic approach, it emphasizes that a good approximation to a perfect metric is sufficient for purposes of designing a combinatorial screening library as well as in other situations where the ability to discriminate molecular structural difference and similarities is required. A perfect descriptor (certainty) for pharmacological searching is not needed to achieve the required level of confidence as long as it is valid (maps a subspace where biological properties cluster).
The typical prior art approach for establishing selection criteria for screening library subsets relied on the following clustering paradigm: 1) characterization of compounds according to a chosen descriptor(s) (metric[s]); 2) calculation of similarities or “distances” in the descriptor (metric) between all pairs of compounds; and 3) grouping or clustering of the compounds based on the descriptor distances. The idea behind the paradigm is that, within a cluster, compounds should have similar activities and, therefore, only one or a few compounds from each cluster, which will be representative of that cluster, need be included in a library. The actual clustering is done until the prior art user feels comfortable with the groupings and their spacing. However, with no knowledge of the validity/usefulness of the descriptor employed, and no guidance with respect to the size or spacing of clusters to be expected from any given descriptor, prior art clustering has been, at best, another intuitive “seat of the pants” approach to diversity measurement.
The prior art describes the construction and application of many molecular structural descriptors while all the while tacitly acknowledging that little progress has been made towards solving the fundamental problem of establishing their validity. The field has nevertheless proceeded based on the belief/faith that, by incorporating in the descriptors certain measures which had been recognized in QSAR studies as being important contributors to defining structure-activity relationships, valid/useful descriptors would be produced. In a leading method representative of this prior art approach to defining a similarity descriptor, E. Martin et al.3 construct a metric for quantifying structural similarity using measures that characterize lipophilicity, shape and branching, chemical functionality, and receptor recognition features. (For the reasons set forth later in relation to the present invention, Martin et al. applied their metric to the reactants which would be used in combinatorial synthesis.) This large set of measures is used to generate a statistically blended metric consisting of a total of 16 properties for each individual reactant studied (5 shape descriptors, 5 measures of chemical functionality, 5 receptor binding descriptors, and one lipophilicity property). This generates a 16 dimensional property space. The 16 properties are simultaneously displayed in a circular “Flower Plots” graphical environment, where each property is assigned a petal. All the plots together visually display how the diversity of the studied reactants is distributed through the computed property space. Martin acknowledges that the plots “. . . cannot, of course, prove that the subset is diverse in any ‘absolute’ sense, independent of the calculated properties.” (id. at 1434)***
In another approach relating to peptoid design, Martin et al.4 have characterized the varieties of shape that an unknown receptor cavity might assume by a few assemblages of blocks, called “polyominos”. Candidates for a combinatorial design are classified by the types of polyominos into which they can be made to fit, or “docked”. The 7 flexible polyomino shape descriptors are added to the previously defined 16 descriptors to yield a 23 dimensional property space. Martin has demonstrated that the docking procedure generates for a methotrexate ligand in a cavity of dihydrofolate reductase nearly the correct structure as that established by X-ray diffraction studies. The docking procedure, which must be applied to every design candidate for each polyomino, requires a considerable amount of CPU time (is computationally expensive). However, a problem with this approach is the conceptually severe (unjustified) approximation of representing all possible irregularly shaped receptor cavities by only about a dozen assemblies of smooth-sided polyomino cubes. Martin has also presented no validation of the approach, which in this case, would be a demonstration that molecules which fit into the same polyominos tend to have similar biological properties.
One approach which has been taken to try to empirically assess the relative validity of prior art metrics has been to survey the metrics to see if any of them appeared to be superior to any others as judged by clustering analysis. Y. C. Martin et al.5 have reported that 3D fingerprints, collections of fragments defined by pairs of atoms and their accessible interatomic distances, perform no better than collections of 2D fragments in defining clusters that separate biologically active from inactive compounds. As will be seen later, some of this work pointed towards the possible validity of one metric, but the authors concentrated on the comparative clustering aspects and did not follow up on the broader import of the data.
W. Herndon6 among others has pointed out that an experimentally determined similarity QSAR is, by definition, a good test of the validity of that similarity concept for the biological system from which it is derived and may have some usefulness in estimating diversity for that system. However, QSARs essentially map only the space of a particular receptor, do not provide information about the validity of other descriptors, and would be generally inapplicable to construction of a combinatorial screening library designed for screening unknown receptors or those for which no QSAR data was available.
Finally, D. Chapman et al.7 have used their “Compass” 3D-QSAR descriptor which is based on the three dimensional shape of molecules, the locations of polar functionalities on the molecules, and the fixation entropies of the molecules to estimate the similarity of molecules. Essentially, using the descriptor, they try to find the molecules which have the maximum overlap (in geometric/cartesian space) with each other. The shape of each molecule of a series is allowed to translate and rotate relative to each other molecule and the internal degrees of freedom are also allowed to rotate in an iterative procedure until the shapes with greatest or least overlap similarity are identified. Selecting 20 maximally diverse carboxylic acids based on seeking the maximally diverse alignment of each of the 3000 acids considered took approximately 4 CPU computing weeks by their method. No indication was given of whether their descriptor was valid in the sense defined above, and, clearly, such a procedure would be too time consuming to apply to a truly large combinatorial library design.
One way in which many of the prior art approaches attempt to work around the problem of not knowing if a molecular structural descriptor is valid is to try, when clustering, to maximize as much as possible the distance between the clusters from which compounds will be selected for inclusion in the screening library subset. The thinking behind this approach is that, if the clusters are far enough apart, only molecules diverse from each other will be chosen. Conversely, it is thought that, if the clusters are close together, oversampling (selection of two or more molecules representative of the same elements of diversity) would likely occur. However, as we have seen, if the metric used in the cluster analysis is not initially valid (does not define a subspace in which molecules with similar biological activity cluster), then no amount of manipulation will prevent the sample from being essentially random. Worse yet, an invalid metric might not yield a selection as good as random! The acknowledgement by Martin quoted above is a recognition of the prior art's failure to yet discover a general method for validating descriptors.
Another related problem in the prior art is the failure to have any objective manner of ascertaining when the library subset under design has an adequate number of members; that is, when to stop sampling. Clearly, if nothing is known about the distribution of the diversity of molecules, one arbitrary stopping point is as good as any other. Any stopping point may or may not sample sufficiently or may oversample. In fact, the prior art has not recognized a coherent quantitative methodology for determining the end point of selection. Essentially, in the prior art, a metric is used to maximize the presumed differences between molecules (typically in a clustering analysis), and a very large number of molecules are chosen for inclusion in a screening library subset based on the belief that there is safety in numbers; that sampling more molecules will result in sampling more of the diversity of a combinatorially accessible chemical space. As pointed out earlier, however, only by including all possible molecules in a library will one guarantee that all of the diversity has been sampled. Short of such total sampling, users of prior art library subsets constructed along the lines noted above do not know whether a random sample, a representative sample, or a highly skewed sample has been screened.
Several other problems flow from the inability to rationally select a combinatorial screening library for optimal diversity and these are related both to the chemistry used to create the combinatorial library and the screening systems used. First, because many more molecules may have to be synthesized than may be needed, mass synthetic schemes have to be devised which create many combinations simultaneously. In fact, there is a good deal of disagreement in the prior art as to whether compounds should be synthesized individually or collectively or in solution or on solid supports. Within any synthetic scheme, an additional problem is keeping track of and identifying the combinations created. It should be understood that, where relatively small (molecular weight of less than about 1500) organic molecules are concerned, generally standard, well known, organic reactions are used to create the molecules. In the case of peptide like molecules, standard methods of peptide synthesis are employed. Similarly for polysaccharides and other polymers, reaction schemes exist in the prior art which are well known and can be utilized. While the synthesis of any individual combinatorial molecule may be straightforward, much time and effort has been and is still being expended to develop synthetic schemes in which hundreds, thousands, or tens of thousands of combinatorial combinations can be synthesized simultaneously.
In many synthetic schemes, mixtures of combinatorial products are synthesized for screening in which the identity of each individual component is uncertain. Alternatively, many different combinatorial products may be mixed together for simultaneous screening. Each additional molecule added to a simultaneous screen means that many fewer individual screening operations have to be performed. Thus, it is not unusual that a single assay may be simultaneously tested against up to 625 or more different molecules. Not until the mixture shows some activity in the biological screening assay will an attempt be made to identify the components. Many approaches in the prior art therefore face “deconvolution” problems; ie. trying to figure out what was in an active mixture either by following the synthetic reaction pathway, by resynthesizing the individual molecules which should have resulted from the reaction pathway, or by direct analysis of duplicate samples. Some approaches even tag the carrier of each different molecule with a unique molecular identifier which can be read when necessary. All these problems are significantly decreased by designing a library for optimal diversity.
Another major problem with the inclusion of multiple and potentially non-diverse compounds in the same screening mixture is that many assays will yield false positives (have an activity detected above a certain established threshold) due to the combined effect of all the molecules in the screening mixture. The absence of the desired activity is only determined after expending the time, effort, and expense of identifying the molecules present in the mixture and testing them individually. Such instances of combined reactivity are reduced when the screening mixture can be selected from molecules belonging to diverse groups of an optimally designed library since it is not as likely that molecules of different (diversity) structures would likely produce a combined effect.
It is clear that a great deal of cleverness has been expended in actually manufacturing the combinatorial libraries. While the basic chemistry of synthesizing any given molecule is straight forward, the next advance in the development of combinatorial chemistry screening libraries will be optimization of the design of the libraries.
Further problems in the prior art arise in the attempt to follow up leads resulting from the screening process. As noted above, many libraries are designed with some knowledge of the receptor and its binding requirements. While, within those constraints, all possible combinatorial molecules are synthesized for screening, finding a few molecules with the desired activity among such a library yields no information about what active molecules might exist in the universe accessible with the same combinatorial chemistry but outside the limited (receptor) library definition. This is an especially troubling problem since, from serendipitous experience, it is well known that sometimes totally unexpected molecules with little or no obvious similarity to known active molecules exhibit significant activity in some biological systems. Thus, even finding a candidate lead in a library whose design was based on knowledge of the receptor is no guarantee that the lead can be followed to an optimal compound. Only a rationally designed combinatorial screening library of optimal diversity can approach this goal.
For prior art library subsets designed around the use of some descriptor to cluster compounds, similar problems may exist. In such a library design, one or at most a few compounds will have been selected from each cluster. Only if the descriptor is valid, does such a selection procedure make sense. If the descriptor is not valid, each cluster will contain molecules representative of many different diversities and selecting from each cluster will still have resulted in a random set of molecules which do not sample all of the diversity present. Since the prior art does not possess a generally applicable method of validating descriptors, all screening performed with prior art libraries is suspect and may not have yielded all the useful information desired about the larger chemical universe from which the library subsets were selected.
Finally, as the expense in time and effort of creating and screening combinatorial libraries increases, the question of the uniqueness of the libraries becomes ever more critical. Questions can be asked such as: 1) does library “one” cover the same diversity of chemical structures as library “two”; 2) if libraries “one” and “two” cover both different and identical aspects of diversity, how much overlap is there; 3) what about the possible overlap with libraries “three”, “four”, “five”, etc.? To date, the prior art has been unable to answer these questions. In fact, assumptions have been made that as long as different chemistries were involved (ie., proteins, polysaccharides, small organic molecules), it was unlikely that the same diversity space was being sampled. However, such an assumption contradicts the well known reality that biological receptors can recognize molecular similarities arising from different structures. When screening for compounds possessing activity for undefined biological receptors, there is no way of telling a priori which chemistry or chemistries is most likely to produce molecules with activity for that receptor. Thus, screening with as many chemistries as possible is desired but is only really practical if redundant sampling of the same diversity space in each chemistry can be avoided. The prior art has not provided any guidance towards the resolution of these problems.